Abstract
In this chapter, two methods of examining the relationships among a set of variables will be examined. The first, principal components analysis (PCA), is essentially a method of data reduction that aims to reduce the dimensionality of multivariate data and, thus, aid in its understanding. The second technique to be discussed is exploratory factor analysis, which has somewhat similar aims to principal components analysis, but in addition tries to uncover something more fundamental about the data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer Science+Business Media New York
About this chapter
Cite this chapter
Everitt, B., Rabe-Hesketh, S. (2001). Principal Components and Factor Analysis. In: Analyzing Medical Data Using S-PLUS. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-3285-6_19
Download citation
DOI: https://doi.org/10.1007/978-1-4757-3285-6_19
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-3176-4
Online ISBN: 978-1-4757-3285-6
eBook Packages: Springer Book Archive